EMPIAR-10196 - CryoEM structure of human LRRC8A [5805 multi-frame micrographs composed of 40 frames each in TIFF format]

EMPIAR-10196

CryoEM structure of human LRRC8A [5805 multi-frame micrographs composed of 40 frames each in TIFF format]

Publication:

Cryo-EM structures of the human volume-regulated anion channel LRRC8

Nureki O, Kasuya G, Nakane T, Yokoyama T, Jia Y, Inoue M, Watanabe K, Nakamura R, Nishizawa T, Kusakizako T, Tsutsumi A, Yanagisawa H, Dohmae N, Hattori M, Ichijo H, Yan Z, Kikkawa M, Shirouzu M, Ishitani R

Nature structural & molecular biology (2018)

PMID: 30127360

DOI: 10.1038/s41594-018-0109-6

Related EMDB entry:

EMD-6952

Deposited:

2018-06-16

Released:

2018-08-23

Last modified:

2018-08-23

Imageset size:

2.89 TB

Imageset DOI:

10.6019/EMPIAR-10196

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned movies of human LRRC8A

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

5805

Image size:

(7420, 7676)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(1.49, 1.49)

Details:

Movies were collected on Talos Arctica at 0.745 A / super-resolution pixel (that is, 1.49 A / physical pixel). Gain references must be rotated and flipped. In MotionCor2, this is "-RotGain 1 -FlipGain 2". Seven datasets were combined to yield the structure.

Dose rate (shown on SerialEM) is:
Dataset 0: 1.264 e/A2/frame
Dataset 1, 2, 3: 1.245 e/A2/frame
Dataset 4: 1.260 e/A2/frame
Dataset 5: 1.257 e/A2/frame
Dataset 6: 1.265 e/A2/frame

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Citations

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.

He L, Bartesaghi A. (2026)

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Deng Y, Wang S, Xiang M, Li Y, Zhuo L, Cao D, Fu X, Zou Q. (2026)

Chart builder: an interactive tool for user driven data visualization in the electron microscopy data bank.

Fonseca N, Duraisamy AK, Wang Z, Somasundharam S, Tayebinia M, de Oliveira LC, Ma M, Turner J, Patwardhan A, Kleywegt GJ, Hartley M, Morris KL. (2026)

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

Gyawali R, Dhakal A, Wang L, Cheng J. (2026)

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.

Jones HN, Deshmukh A, Pande K. (2026)

CryoGS: High-Quality Cryo-EM Homogeneous Reconstruction by Gaussian Splatting

Zeng B, Liu S, Cheng S, Xu G, Fan H. (2026)

Point cloud deformation modeling for particle selection following cryo-EM 2D classification.

Wang X, Mo Z, Li F, Zhang F, Wan X. (2026)

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Kishore V, Debarnot V, Righetto RD, Engel BD, Dokmanić I. (2026)

CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection.

Schäfer JH, Calza A, Hom K, Damodar P, Peng R, Bogdanović N, Lander GC, Stagg SM, Cianfrocco MA. (2025)